In this paper the design and optimization of a nonlinear diameter taper, connecting the output section of a gyrotron cavity to the uniform output waveguide section, is presented. The design of a nonlinear taper of a 42 GHz, 200 kW CW gyrotron operating in the TE 0,3 cavity mode with axial output collection has been taken as a case study. The taper synthesis has been carried out considering a raised cosine type of nonlinear taper and the analysis is done using a dedicated scattering matrix code. In addition, an improved particle swarm optimization -an evolutionary optimization -algorithm is used for the design optimization of this nonlinear taper. The optimum design of the taper shows the effectiveness of the presented method.
Heart disease can be determined by the calculating regional and global wall motion of the left ventricular (LV). In this research, we designed a dynamic simulation tool using Computed Tomography (CT) images that helps to find the difference between actual and simulated left ventricular functions. In this study, thirteen healthy subjects were involved with actual and simulated left ventricular functions. We obtained the high correlation between actual left ventricular wall motion (ALVWM) and simulated left ventricular wall motion (SLVWM) which is (r = 0.99). Our results validate that our simulation tool is feasible for simulating left ventricular motion.
Manual segmentation of brain tissues from MR images for diagnosis purpose is time consuming and requires much effort even by experts. This has motivated generation of automated segmentation techniques. Moreover, due to presence of noise in an image and its low contrast, it is difficult to correctly delineate tumour from brain MR images. In this paper, a novel hybrid method using Gaussian Mixture Model based Hidden Markov Random Field (HMRF) with Expectation Maximization (EM) has been proposed which segments tissues from MR brain images efficiently and helps to separate out tumour area easily. The proposed method minimizes energy function during each iteration of EM and gives comparable results with ground truth. The results obtained are also compared with the results of fuzzy cmeans clustering algorithm for image segmentation.
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